Power Query Type Number

To effectively handle numerical data in Power Query and Power BI, understanding and correctly applying the Power Query type number is crucial. This involves not just converting data, but also knowing the nuances between different number types like type number (decimal), Int64.Type (whole number), and type nullable number, and how they impact your data models and reports. Here’s a quick guide to managing numerical types in Power Query:

Step 1: Identify Your Data Type Needs. Before converting, determine if your column should be a whole number (e.g., OrderID, Quantity) or a decimal number (e.g., Price, Sales). This decision impacts performance and accuracy.
Step 2: Access the Query Editor. In Power BI Desktop, navigate to “Transform Data” to open the Power Query Editor.
Step 3: Select the Column(s). Click on the header of the column you wish to change. For power query data type number and text scenarios, this is especially important as mixed types often default to text.
Step 4: Change Data Type.
- Using the UI: Click the data type icon (often a small ‘ABC’ or ‘123’) next to the column name in the Query Editor. From the dropdown, select “Decimal Number” (for type number or power bi type decimal number), “Whole Number” (for Int64.Type), or “Fixed Decimal Number” (for currency).
- Using M Code (Advanced): If you prefer to write M code, which is more robust for complex transformations, you can use Table.TransformColumnTypes. For example, to change a column named “Sales” to a number, you would add a step like: Table.TransformColumnTypes(Source, {{"Sales", type number}}) or Table.TransformColumnTypes(Source, {{"Sales", Int64.Type}}) for whole numbers.
Step 5: Handle Errors and Nulls. When performing power query change type to number, errors (e.g., text values that cannot be converted) will appear as Error. You can right-click the column header, select “Remove Errors” or “Replace Errors” to handle them. For power query type nullable number, the type nullable number implicitly allows nulls.
Step 6: Address “2 Decimal” Formatting (Power BI). For power query type number 2 decimal, remember that setting decimal places like two is primarily a display format in Power BI, not a data type in Power Query itself. In Power Query, type number handles all decimals. You set the display format in the Power BI “Modeling” tab or “Format” pane.
Step 7: Check Type Conditionally (power query if type is number). To inspect or conditionally transform data based on its type, you can use Value.Is(value, type) in a custom column. For instance, Value.Is([YourColumn], type number) returns true if the value is a number. This is useful for identifying power bi data type number and text columns before conversion.

Table of Contents

Understanding Numerical Data Types in Power Query

When you’re wrangling data in Power Query, one of the foundational steps is ensuring your columns have the correct data types. This isn’t just about making your data look pretty; it’s about enabling accurate calculations, efficient data loading, and robust report generation in Power BI. Think of it like organizing your pantry: you wouldn’t mix flour with sugar, just as you wouldn’t mix text with numbers if you intend to perform mathematical operations.

The type number in Power Query, often represented as “Decimal Number” in the Power BI user interface, is the most versatile numeric type. It’s a double-precision floating-point number, meaning it can handle both whole numbers and decimal values with high precision. This is your go-to for financial figures, measurements, or any value that might contain fractions. On the other hand, Int64.Type, appearing as “Whole Number” in Power BI, is designed for integers—numbers without any fractional component. This is ideal for IDs, counts, or quantities where decimals are not expected. Choosing correctly between these two can significantly impact memory usage and query performance, especially with large datasets. For instance, if you have a column of product IDs that are always whole numbers, using Int64.Type is more efficient than type number.

Beyond these common types, Power Query also offers type currency (Fixed Decimal Number in Power BI), which is specifically designed for monetary values, providing fixed-point precision to avoid rounding errors common with floating-point numbers. There’s also type text, which is crucial to understand because Power Query often defaults to this type if a column contains even a single non-numeric character. This becomes a “power query data type number and text” scenario, requiring explicit conversion if you want to use the column for calculations. Furthermore, type nullable number is a variation of type number that explicitly permits null values, which is the default behavior when Power Query infers a number type unless specified otherwise. This is vital when your data sources might have missing numerical entries. Correctly managing these types ensures data integrity and paves the way for powerful analytics.

Diving Deep into `type number` (Decimal Number)

The type number in Power Query is your workhorse for most numerical data that might involve decimals. In Power BI, this translates to the “Decimal Number” data type. It uses an 8-byte, double-precision floating-point format, capable of storing a vast range of values with high precision.

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Understanding its Precision and Range:
- Precision: type number offers approximately 15-17 decimal digits of precision. This means it can accurately represent very small or very large numbers with many decimal places. For most business calculations, like sales figures, prices, or measurements, this level of precision is more than sufficient.
- Range: The range for type number is approximately ±1.79 x 10^308 to ±2.23 x 10^-308. This enormous range ensures you can handle virtually any numerical value encountered in real-world datasets.
- Example: A SalesAmount column with values like 123.45, 99.995, or 1500000.75 would ideally be type number.
When to Use type number:
- Financial Data: Prices, revenues, costs, profits, and percentages.
- Measurements: Weights, heights, lengths, temperatures, and quantities that can be fractional.
- Averages and Ratios: Any calculation that might result in a non-integer value.
- Mixed Whole and Decimal Data: If a column could contain both whole numbers and decimals, type number is the safest bet to avoid data loss during conversion.
Common Scenarios and M Code Examples:
- Basic Conversion: To change a column named [Revenue] to type number:
```
Table.TransformColumnTypes(Source,{{"Revenue", type number}})
```
- Handling power query type number 2 decimal: While Power Query’s type number handles all decimals, the “2 decimal” requirement typically refers to display formatting in Power BI. You’d convert to type number in Power Query, then format in Power BI.
```
// In Power Query:
Table.TransformColumnTypes(Source,{{"Price", type number}})
// In Power BI Desktop: Select the 'Price' column, go to 'Column tools' or 'Modeling' tab,
// and set 'Format' to 'Currency' or 'Number' with 2 decimal places.
```
- Converting from Text: If your data source imports numbers as text (e.g., “123.45” as text), this is a critical step.
```
Table.TransformColumnTypes(Source,{{"TextualValue", type number}})
```
  - Pro Tip: If you encounter errors during this conversion due to non-numeric text, first try Value.FromText([TextualValue]) in a custom column, then convert that new column to type number. This allows you to inspect which values are causing errors.
- power bi type number: In Power BI, when you see “Decimal Number” in the data type dropdown, that’s type number from Power Query’s perspective. It’s the standard for general numeric data.

Choosing type number appropriately ensures your calculations are precise and your data model is robust for analytical tasks in Power BI.

`Int64.Type` vs. `type number`: Choosing the Right Whole Number Type

The choice between Int64.Type and type number is a critical decision in Power Query that impacts performance, memory usage, and data integrity. While type number is a general-purpose floating-point number, Int64.Type is specifically designed for whole numbers (integers).

Int64.Type Explained (Whole Number):
- Purpose: Int64.Type represents a 64-bit signed integer. This means it can store whole numbers (positive, negative, and zero) without any fractional component.
- Range: Its range is approximately from -9,223,372,036,854,775,808 to 9,223,372,036,854,775,807. This is a massive range, accommodating almost any whole number you’ll encounter.
- Memory and Performance: Integers generally consume less memory and allow for faster mathematical operations compared to floating-point numbers. This can be a significant advantage when dealing with very large datasets (millions or billions of rows).
- Power BI Equivalent: In Power BI, Int64.Type is recognized as “Whole Number.”
When to Use Int64.Type:
- Identifiers: OrderID, CustomerID, ProductKey, EmployeeID. These are almost always whole numbers.
- Counts: NumberOfUnits, TotalTransactions, CustomerCount.
- Years, Months, Days: When representing dates or time components as integers.
- Ages: When age is stored as whole years.
- Strictly Whole Numbers: If you are absolutely certain a column will never contain a decimal, Int64.Type is the ideal choice.

Key Differences and Why They Matter (power query int64 type vs number):

Feature	`Int64.Type` (Whole Number)	`type number` (Decimal Number)
Type	Signed 64-bit integer	Double-precision floating-point
Decimals	No (truncates or rounds if fractional data is provided)	Yes (retains decimal precision)
Memory	Generally less (8 bytes)	Generally more (8 bytes, but internal handling can differ)
Performance	Faster for integer arithmetic	Slower for integer arithmetic due to floating-point overhead
Precision	Exact representation of whole numbers within its range	Approximate representation for some decimal numbers (floating-point issues)
Power BI UI	Whole Number	Decimal Number

Conversion and M Code Examples:
- To convert a column named [ProductID] to Int64.Type:
```
Table.TransformColumnTypes(Source,{{"ProductID", Int64.Type}})
```
- Consider a scenario where you have a Quantity column. If it’s always whole units:
```
Table.TransformColumnTypes(Source,{{"Quantity", Int64.Type}})
```
  If Quantity could be 0.5 (half units), then type number would be necessary.
- Warning: If you convert a type number that contains decimal values (e.g., 123.45) to Int64.Type, Power Query will round or truncate the decimal part depending on the specific conversion function used implicitly or explicitly. For example, direct type conversion usually truncates. Always ensure your data is appropriate for Int64.Type to avoid data loss.

By thoughtfully selecting between Int64.Type and type number, you optimize your data model for both efficiency and accuracy, avoiding potential pitfalls of imprecise calculations or unnecessary memory consumption.

Managing Mixed Data Types: `power query data type number and text`

One of the most common headaches in data cleaning is dealing with columns that contain a mix of numbers and text. This often happens when data is manually entered, comes from diverse sources, or includes error messages within numerical fields. Power Query, by default, is quite smart, but when it encounters power query data type number and text values, it typically infers the column as type text to avoid data loss. However, to perform any numerical operations or aggregations, you must convert these columns to a numeric type.

Why Mixed Types are an Issue:
- Calculation Errors: You cannot perform mathematical operations (addition, subtraction, average, etc.) on text data. If you try, Power BI will throw an error or produce incorrect results.
- Filtering and Sorting: Numeric sorting and filtering will not work as expected; text sorting will place “100” before “20” because ‘1’ comes before ‘2’.
- Data Model Bloat: Storing numbers as text can be less efficient in terms of memory.
- Visualization Limitations: Charts and graphs designed for numerical data will not function correctly.
Common Scenarios for Mixed Types:
- Manual Entry Errors: Users accidentally type “N/A” or “See Notes” in a numerical column.
- Error Codes: A system might output “Error 404” instead of a numeric value.
- Units or Symbols: Numbers stored with units like “100 pcs” or currency symbols “£50.00”.
- Blank Cells: Sometimes blank cells are interpreted as text during import.
Strategies to Clean and Convert:
1. Identify Non-Numeric Values:
  - Method 1: “Replace Errors” (UI): Right-click the column header -> “Replace Errors”. You can replace errors with null or a specific numeric value (e.g., 0). This is useful if you expect legitimate numbers but know some entries will fail conversion.
  - Method 2: “Filter Out Errors” (UI): If you convert a column and errors appear, click the green bar (error indicator) on the column header and select “Remove Errors”. Be cautious, as this will delete rows.
  - Method 3: Create a Custom Column for Inspection (M Code): Before converting, add a custom column to identify non-numeric values. This helps diagnose the problem.
```
Table.AddColumn(Source, "IsNumberCheck", each try Number.From([YourColumnName]) otherwise "Not a Number")
```
    Then, filter this new IsNumberCheck column to see all values that couldn’t be converted.
2. Clean the Data:
  - Remove Text (if unnecessary): If the text is extraneous (e.g., ” pcs”), use “Replace Values” to remove it.
  - Extract Numbers: Use functions like Text.Select or Text.Remove with regular expressions (if advanced enough) to extract only the numeric characters.
```
// Example: Remove non-numeric characters (except decimals)
Table.TransformColumns(Source, {{"ValueWithUnits", each Text.Select(_, {"0".."9", "."}), type text}})
```
  - Conditional Logic (M Code): For complex scenarios, use if statements to handle different cases.
```
Table.TransformColumns(Source, {{"MixedColumn", each
    if Value.Is(_, type number) then _ // Already a number
    else if Value.Is(Text.From(_), type text) and Text.Length(Text.Select(Text.From(_), {"0".."9", "."})) > 0 then Number.From(Text.Select(Text.From(_), {"0".."9", "."})) // Extract number from text
    else null // Or 0, or handle as needed
, type nullable number}})
```
    - Explanation: This M code snippet tries to preserve existing numbers, then attempts to extract numbers from text strings, and finally sets any remaining non-convertible values to null. This is a robust way to power query change type to number from mixed data.
3. Apply Type Conversion: Once the column is clean, apply the desired number type (type number or Int64.Type).
```
Table.TransformColumnTypes(Source,{{"CleanedValue", type number}})
```
- power bi data type number and text: When you load data into Power BI from Power Query, if a column is type text but contains numbers, Power BI will treat it as a text field. To ensure it’s a numeric field in Power BI, the conversion must happen in Power Query.

Mastering these cleaning and conversion techniques for mixed data types is fundamental for anyone working with Power Query, transforming raw, messy data into usable, analytical assets.

`power query type nullable number`: Handling Missing Numerical Values

In real-world datasets, missing values are a common occurrence. For numerical columns, these missing values often appear as blanks, empty strings, or specific placeholder text like “N/A.” When Power Query infers a column’s data type or when you explicitly set it, the concept of “nullable” becomes critically important.

What is type nullable number?
- It’s a specific data type that explicitly indicates that a column can contain both numeric values (type number) and null values.
- In Power Query M language, type number implicitly allows null values by default unless you explicitly specify type nonnullable number. However, explicitly using type nullable number can sometimes improve clarity in your M code, especially when you are being very precise about your data contract.
- Power BI Equivalent: In Power BI, when you see a column set to “Decimal Number” or “Whole Number,” it automatically allows nulls. There isn’t a separate UI option for “nullable number” because it’s the standard behavior for these types.
When nullable number is Important:
- Incomplete Data: When your source system doesn’t always provide a value for a specific numeric field (e.g., optional DiscountAmount, MiddleInitial where null is expected).
- Error Handling: When you use try...otherwise blocks in M code to gracefully handle conversion errors, the otherwise part often results in null if a conversion fails.
```
// Example: Convert text to number, replace errors with null
Table.TransformColumns(Source, {{"TextualSales", each try Number.From(_) otherwise null, type nullable number}})
```
- Merging Queries: When merging tables, if a join doesn’t find a match, numeric columns from the unmatched side will often result in null values.
Implications of Nulls on Calculations:
- Aggregations: Functions like SUM, AVERAGE, MIN, MAX in Power BI (and Power Query) generally ignore null values. This is usually the desired behavior. For example, AVERAGE will calculate the average of only the non-null values.
- Arithmetic Operations: Any arithmetic operation (e.g., addition, multiplication) involving a null value will result in null. For example, 10 + null equals null.
- Filtering: Filtering for null values is a common operation to identify missing data or errors.
Handling Nulls (M Code Examples):
1. Replace Nulls with a Specific Value: If null should be treated as 0 for certain calculations, you can replace them.
```
Table.ReplaceValue(Source, null, 0, Replacer.ReplaceValue, {"Quantity"})
```
  Then, convert to type number or Int64.Type as needed.
```
Table.TransformColumnTypes(ReplaceNulls,{{"Quantity", Int64.Type}})
```
2. Filter Out Nulls: If rows with missing numerical data are not relevant to your analysis, you can remove them.
```
Table.SelectRows(Source, each [SalesAmount] <> null)
```
3. Conditional Logic with Nulls: Use if [Column] = null then ... else ... for advanced handling.
```
Table.AddColumn(Source, "AdjustedSales", each if [SalesAmount] = null then 0 else [SalesAmount] * 1.05, type number)
```
4. Ensuring Nullability (explicitly if needed): While type number is implicitly nullable, sometimes you might see type nullable number generated by Power Query or want to be explicit.
```
Table.TransformColumnTypes(Source,{{"OptionalValue", type nullable number}})
```

Understanding type nullable number empowers you to robustly handle imperfect data, ensuring your transformations and calculations are accurate and account for potential gaps in your numerical information.

`power query change type to number`: Step-by-Step Conversion and Error Handling

Changing a column’s data type to a number in Power Query is one of the most frequent transformations you’ll perform. It’s often the first step to enable numerical analysis. However, it’s also where many errors can pop up if your data isn’t clean. Let’s break down the process and how to handle common pitfalls.

The Basics of Type Conversion:
- UI Approach: The easiest way to power query change type to number is through the Power Query Editor’s user interface.
  1. Select the column header (e.g., a column currently showing ‘ABC 123’ indicating text or ‘123’ for a number, but you want to change its specific numeric type).
  2. Click on the small icon to the left of the column name (it often shows ‘ABC’ for text, ‘1.2’ for decimal, or ‘1’ for whole number).
  3. From the dropdown menu, select the desired numeric type:
    - Decimal Number (1.2): This converts to type number in M.
    - Fixed Decimal Number (1.23): Converts to type currency.
    - Whole Number (123): Converts to Int64.Type.
  4. Power Query will automatically insert a Table.TransformColumnTypes step in your “Applied Steps” pane.
- M Code Approach (More Control): For precise control, especially when automating transformations or dealing with multiple columns, writing the M code directly is powerful.
```
// To change a single column "SalesAmount" to type number
let
    Source = YourPreviousStep,
    #"Changed Type" = Table.TransformColumnTypes(Source,{{"SalesAmount", type number}})
in
    #"Changed Type"

// To change multiple columns to their respective number types
let
    Source = YourPreviousStep,
    #"Changed Multiple Types" = Table.TransformColumnTypes(Source, {
        {"Price", type number},
        {"Quantity", Int64.Type},
        {"Discount", type nullable number} // Example for nullable
    })
in
    #"Changed Multiple Types"
```
Common Error Scenarios During Conversion:
- “Data Format Error” / “Could not convert to number”: This is the most frequent issue. It means Power Query found values in your column that it cannot interpret as numbers.
  - Causes: Textual entries (“N/A”, “-“, “See notes”), hidden spaces, currency symbols ($), commas (if not set as decimal separator in your locale), multiple decimal points, or completely irrelevant text.
  - Symptoms: After conversion, you’ll see “Error” values in the cells. The column header will often show a red bar indicating errors.
Robust Error Handling Techniques:
1. “Replace Errors” Feature (UI / M Code):
  - UI: Right-click the column header -> “Replace Errors”. You can replace errors with null (recommended default for missing numerical data) or a specific value like 0 if that’s appropriate for your analysis.
  - M Code: Table.ReplaceErrorValues(YourPreviousStep, {{"ColumnName", null}}). This step should usually come after the Table.TransformColumnTypes step.
```
let
    Source = Csv.Document(File.Contents("C:\Data.csv"),[Delimiter=",", Columns=2, Encoding=65001, QuoteStyle=QuoteStyle.Csv]),
    #"Promoted Headers" = Table.PromoteHeaders(Source, [PromoteAllScalars=true]),
    #"Changed Type with Errors" = Table.TransformColumnTypes(#"Promoted Headers",{{"Sales", type number}}),
    #"Replaced Errors" = Table.ReplaceErrorValues(#"Changed Type with Errors", {{"Sales", null}})
in
    #"Replaced Errors"
```
2. Using try...otherwise (M Code for Granular Control): This is a powerful M function for conditional conversion. It attempts an operation, and if it fails, it returns the value specified in otherwise. This allows you to handle conversion failures on a cell-by-cell basis without crashing the query or removing rows.
```
// Example: Convert 'Sales' column to number, if conversion fails, set to null
Table.TransformColumns(Source, {{"Sales", each try Number.From(_) otherwise null, type nullable number}})
```
  - Explanation: Number.From(_) attempts to convert the current cell value _ to a number. If it succeeds, that number is returned. If it fails (e.g., the cell contains text), otherwise null is executed, inserting null into the cell. type nullable number is specified for clarity.
3. Filtering Out Non-Numeric Rows (Use with Caution): If you absolutely need only valid numeric data and erroneous rows are irrelevant, you can filter them out.
  - UI: After attempting conversion and seeing errors, click the error indicator on the column header and choose “Remove Errors”.
  - M Code:
```
// First, convert, which might introduce errors
let
    Source = YourPreviousStep,
    #"Attempted Type Change" = Table.TransformColumnTypes(Source,{{"Sales", type number}}),
    #"Removed Error Rows" = Table.SelectRows(#"Attempted Type Change", each not Value.Is([Sales], type error))
in
    #"Removed Error Rows"
```
  - Caution: This will remove entire rows, potentially losing valuable data from other columns in those rows. Only use if those specific rows are truly unrecoverable or irrelevant.

By mastering these conversion and error-handling techniques, you’ll be well-equipped to transform even the messiest raw data into clean, numerically usable columns in Power Query.

`power query if type is number`: Conditional Logic and Type Checking

Beyond simply converting data types, sometimes you need to perform actions conditionally based on whether a value is a number, text, or some other type. This is where M functions like Value.Is() come into play, allowing you to implement power query if type is number logic. This is particularly useful for data validation, profiling, and complex transformations where data quality is inconsistent.

Why Conditional Type Checking?
- Data Validation: Identify rows that do not conform to expected data types. For example, flagging entries in a Quantity column that are actually text.
- Error Prevention: Before attempting a conversion that might fail, check the type.
- Dynamic Transformations: Apply different logic based on the actual content of a cell. For instance, if a column contains “N/A”, handle it differently than a valid number.
- Profiling and Reporting: Create custom columns that indicate the data type of each cell, helping you understand data quality issues. This is especially useful for columns that exhibit power bi data type number and text behavior before cleaning.
Key M Functions for Type Checking:
1. Value.Is(value, type as type):
  - This function checks if value is compatible with the specified type. It returns true or false.
  - Examples:
    - Value.Is(123, type number) -> true
    - Value.Is("Hello", type text) -> true
    - Value.Is("123", type number) -> false (because “123” is type text, even if it looks like a number)
    - Value.Is(123, Int64.Type) -> true
    - Value.Is(123.45, Int64.Type) -> false (because 123.45 is not a whole number)
2. Value.Type(value):
  - This function returns the actual type of a given value.
  - Example:
    - Value.Type(123) -> type number
    - Value.Type("Hello") -> type text
    - Value.Type(true) -> type logical
Practical Scenarios and M Code Examples:
1. Adding a Column to Indicate if a Value is a Number:
  - This is great for auditing and understanding data quality.
```
Table.AddColumn(Source, "IsNumeric", each Value.Is([MyColumn], type number), type logical)
```
  - You can then filter on the IsNumeric column to quickly identify non-numeric rows.
2. Conditional Conversion based on Type:
  - Suppose you have a SalesValue column that sometimes contains legitimate numbers, and sometimes text like “Pending” or “N/A”. You want to convert numbers and replace text with null.
```
Table.TransformColumns(Source, {{"SalesValue", each
    if Value.Is(_, type number) then _ // If it's already a number, keep it
    else if Value.Is(_, type text) and (Text.Contains(_, "N/A") or Text.Contains(_, "Pending")) then null // If specific text, make null
    else try Number.From(_) otherwise null // Otherwise, try converting, if fails, make null
, type nullable number}})
```
  - Explanation: This advanced example prioritizes, checks if it’s already a number, then checks for specific text values, and as a last resort, attempts to convert anything else, returning null if the conversion fails.
3. Filtering Rows that are NOT Numeric:
  - To clean your dataset by removing rows where a critical column isn’t a valid number.
```
Table.SelectRows(Source, each Value.Is([CriticalNumberColumn], type number) or Value.Is([CriticalNumberColumn], Int64.Type))
// Or to include nullable numbers:
// Table.SelectRows(Source, each Value.Is([CriticalNumberColumn], type number) or Value.Is([CriticalNumberColumn], type nullable number))
// More robust for any numeric type:
// Table.SelectRows(Source, each Value.Type([CriticalNumberColumn]) is type number)
```
4. Checking for Any Numeric Type (power bi type number):
  - When you want to know if a value is any kind of number (decimal, whole, currency). In M, type number is the base for all numeric types. Value.Type(value) is type number will evaluate to true if the value’s type is type number (decimal) or derived types like Int64.Type or type currency.
```
Table.AddColumn(Source, "IsAnyNumeric", each Value.Type([ColumnX]) is type number, type logical)
```

By mastering conditional logic with Value.Is() and Value.Type(), you gain immense power to create dynamic, resilient, and intelligent data transformations in Power Query, ensuring your data adheres to your strict quality standards.

`power bi type decimal number`: Formatting vs. Data Type in Power BI

When you’re working with numbers, especially in Power BI, there’s a common point of confusion: the difference between a data type in Power Query (M) and the display format in Power BI Desktop. The term power bi type decimal number often refers to a field that appears with decimal places, which might seem straightforward but has nuances.

Understanding Data Types in Power Query (M):
- As discussed, in Power Query, the primary type for decimal numbers is type number. This is a robust floating-point type capable of handling many decimal places.
- Int64.Type is for whole numbers.
- type currency is for fixed-decimal numbers (like money), offering precise control over decimals to avoid floating-point errors.
Understanding Formatting in Power BI Desktop:
- Once data is loaded into the Power BI data model, you interact with it in the “Report View” or “Model View.” Here, you assign a display format to columns.
- For a column that is type number (Decimal Number) in Power Query, you can set its display format in Power BI to:
  - General: Shows numbers as-is, potentially with many decimal places.
  - Number: Allows you to specify the exact number of decimal places (e.g., 2, 4, or more), a thousands separator, and whether to show negative numbers in parentheses.
  - Currency: Formats as currency, typically showing 2 decimal places and a currency symbol, adhering to regional settings.
  - Percentage: Displays the number as a percentage (multiplies by 100 and adds a % sign).
The Nuance of power query type number 2 decimal:
- When a user asks for power query type number 2 decimal, they are almost always referring to the display of the number in Power BI, not a distinct data type in Power Query.
- Power Query’s Role: In Power Query, you simply ensure the column is type number. Power Query doesn’t have a specific type number with 2 decimal places. The internal precision of type number is much higher than two decimal places.
- Power BI’s Role: It’s in Power BI Desktop (after loading from Power Query) that you apply the “2 decimal places” formatting.
  1. In Power BI Desktop, select the numeric column in the “Fields” pane.
  2. Go to the “Column tools” tab (or “Modeling” tab in older versions).
  3. In the “Formatting” section, choose “Number” or “Currency” from the dropdown.
  4. Set the “Decimal places” to 2.
Example Scenario:
- You have a Price column in your data source.
- In Power Query: You would convert it to type number.
```
Table.TransformColumnTypes(Source,{{"Price", type number}})
```
- In Power BI Desktop: After loading, you select the Price column. In the “Column tools” tab, you’d set its format to “Currency” or “Number” with 2 decimal places.
  - If a value is 123.4567, Power Query stores it as 123.4567.
  - When displayed in Power BI with “2 decimal places” formatting, it will show as 123.46. The underlying value is still 123.4567, which is important for calculations.
When to use type currency (Fixed Decimal Number):
- While type number can handle decimals, type currency (Fixed Decimal Number in Power BI) is preferred for monetary calculations where exact precision is paramount. Floating-point numbers (type number) can sometimes have tiny rounding errors in complex calculations due to their binary representation of decimal values. type currency uses a fixed-point decimal, which avoids these issues for financial data.
- In Power Query:
```
Table.TransformColumnTypes(Source,{{"TransactionAmount", type currency}})
```
- In Power BI: This will appear as “Fixed Decimal Number” and automatically format with a currency symbol and typically two decimal places.

In summary, remember that Power Query is about defining the true data type and structure, while Power BI is about consuming that data and presenting it effectively, which includes its display formatting. For power bi type decimal number with specific decimal places, you typically convert to type number in Power Query and then apply the desired display formatting in Power BI Desktop.

Performance and Best Practices for Numerical Types

Optimizing the performance of your Power Query transformations and Power BI models is crucial, especially as datasets grow. The data types you choose for your numerical columns play a significant role in this. Applying best practices not only speeds up your queries but also ensures data accuracy and model efficiency.

Choose the Smallest Appropriate Type:
- Principle: Don’t use a hammer to crack a nut. If a column contains only whole numbers and will never exceed the range of Int64.Type, use Int64.Type (Whole Number). It’s generally more memory-efficient and can lead to faster calculations than type number (Decimal Number).
- Example: For OrderID, CustomerID, or Quantity (if always whole units), Int64.Type is the ideal choice. A SalesAmount or Price column, however, would require type number or type currency.
- Impact: Using type number unnecessarily for integer columns means Power BI allocates more memory than needed and performs floating-point arithmetic which is slightly slower than integer arithmetic. For small datasets, this difference is negligible (milliseconds), but for millions or billions of rows, it can accumulate to seconds or even minutes in refresh times.
Type Conversion as Early as Possible:
- Principle: Convert data types to their correct format as soon as your data is clean enough. This is often one of the first few “Applied Steps” in Power Query.
- Why:
  - Query Folding: Early type conversion (especially from text to number) can enable Power Query to “fold” operations back to the data source. Query folding means Power Query translates your M steps into the native query language of the source (e.g., SQL), allowing the source database to do the heavy lifting. This drastically improves performance, especially with large datasets, as less data needs to be pulled into Power Query.
  - Accuracy: Performing calculations on correctly typed columns prevents unexpected errors or incorrect results that can arise from implicit conversions or text-based operations.
  - Clarity: Your query steps are clearer when data types are established early.
Handle Errors Before Type Conversion (If Possible):
- Principle: If you know your numeric column contains non-numeric “junk” values, try to clean or transform those specific values before attempting a blanket type conversion.
- Strategy: Use Table.ReplaceValue, Text.Remove, Text.Select, or if statements with Value.Is() to transform problematic text into null or a default numeric value (like 0) before you apply Table.TransformColumnTypes.
- Benefit: This makes your Table.TransformColumnTypes step cleaner and less prone to introducing “Error” values. While try...otherwise is powerful for handling errors during conversion, pre-cleaning reduces the need for it and signals clearer intent.
Be Mindful of type currency (Fixed Decimal Number):
- For financial values where floating-point inaccuracies are unacceptable (e.g., totals that must exactly match source systems down to the cent), use type currency. This stores values as fixed-point decimals, preventing the minor discrepancies that can sometimes occur with type number.
- Consideration: type currency typically uses more memory than Int64.Type and sometimes even type number due to its fixed precision. Use it where precision is critical, not for every decimal.
Avoid Unnecessary Conversions:
- Each Table.TransformColumnTypes step is an operation. While often necessary, avoid converting a column back and forth or converting types when they are already correct. Review your “Applied Steps” to ensure efficiency.
Impact on Power BI Model Size and Refresh Times:
- Memory Footprint: Improper data types (e.g., using text for numbers, or type number for IDs) can significantly inflate your Power BI model size. A larger model takes longer to load, refresh, and might consume more RAM on the user’s machine.
- Refresh Performance: Power Query transformations run during data refresh. Efficient data types and query folding directly translate to faster refresh cycles, which is critical for timely data updates.

By diligently applying these performance tips and best practices for numerical data types, you’ll build more robust, efficient, and accurate data models in Power Query and Power BI.

FAQ

What is `type number` in Power Query?

type number in Power Query refers to a double-precision floating-point number, which is equivalent to “Decimal Number” in Power BI Desktop. It can store both whole numbers and numbers with decimal places, offering high precision for a wide range of numerical values.

How do I change a column to `type number` in Power Query?

You can change a column’s type via the Power Query Editor UI by clicking the data type icon next to the column name and selecting “Decimal Number”. Alternatively, in M code, use Table.TransformColumnTypes(Source, {{"YourColumn", type number}}).

What is the difference between `type number` and `Int64.Type`?

type number (Decimal Number) is a floating-point type for values with decimals, while Int64.Type (Whole Number) is a 64-bit integer type for whole numbers only. Int64.Type is generally more memory-efficient and faster for integer operations, but type number is necessary for fractional values.

When should I use `Int64.Type` instead of `type number`?

Use Int64.Type for columns that contain strictly whole numbers, such as IDs (OrderID, CustomerID), counts (Quantity, NumberOfItems), or ages. It’s more efficient for these types of data.

How do I convert a column to “2 decimal places” in Power Query?

Power Query’s type number inherently supports decimals. The “2 decimal places” setting is primarily a display format applied in Power BI Desktop, not a distinct data type in Power Query. First, ensure your column is type number in Power Query, then apply the formatting in Power BI Desktop’s “Column tools” or “Modeling” tab. What is online presentation tools

What does `type nullable number` mean in Power Query?

type nullable number indicates that a column can contain both numeric values and null values. By default, most numeric types in Power Query are nullable. Explicitly stating nullable number simply emphasizes this allowance for missing data.

How do I handle `power query data type number and text` issues?

When a column contains a mix of numbers and text, Power Query often defaults it to type text. To convert it to a number, first clean the non-numeric entries (e.g., replace text with null or 0, remove unwanted characters) using “Replace Values” or custom M code with try...otherwise or Text.Select. Then, apply the desired number type.

What is `power bi type decimal number`?

power bi type decimal number refers to the “Decimal Number” data type in Power BI, which corresponds to type number in Power Query. It’s used for any numerical data that might include decimal places.

Can Power Query automatically convert text to number?

Yes, Power Query attempts to automatically convert text to a number if it recognizes a valid numerical format. However, if it encounters any non-numeric characters (other than recognized decimal/thousands separators for your locale), the conversion will fail, resulting in an “Error” value.

How do I check if a value is a number in Power Query (e.g., `power query if type is number`)?

Use the Value.Is(value, type) function in a custom column. For example, Value.Is([YourColumn], type number) returns true if the value in YourColumn is compatible with type number. You can also use Value.Type(value) is type number for a broader check covering all numeric types. Marriage license free online

What happens if I convert a decimal number to `Int64.Type`?

If you convert a column with decimal values (e.g., 123.45) to Int64.Type, Power Query will either truncate or round the decimal part, depending on the specific conversion method or implicit behavior. This can lead to data loss, so always verify your data before such conversions.

How do I replace errors after a type conversion in Power Query?

After a Table.TransformColumnTypes step, you can right-click the column header in the Power Query Editor and select “Replace Errors.” You can then choose to replace errors with null, 0, or any other appropriate value. In M code, use Table.ReplaceErrorValues(PreviousStep, {{"YourColumn", null}}).

Why is my number column showing “ABC 123” icon in Power Query?

The “ABC 123” icon means Power Query has identified the column as a mixed data type (containing both text and numbers) and has defaulted it to type text. You must explicitly change its type to a number to perform calculations.

Is `type currency` better than `type number` for financial data?

Yes, type currency (Fixed Decimal Number in Power BI) is often better for critical financial data. It uses a fixed-point decimal representation, which prevents tiny floating-point inaccuracies that can sometimes occur with type number in complex calculations, ensuring exact sums.

How does changing data types affect Power BI performance?

Correct data type selection, especially using Int64.Type where appropriate, can significantly improve Power BI model size and refresh performance. Smaller, more efficient data types consume less memory and allow for faster query folding and calculations. Royalty free online

Can I change `power bi data type number and text` directly in Power BI Desktop?

While you can change a column’s data type in Power BI Desktop’s “Data View” or “Model View,” it’s best practice to perform these transformations in Power Query Editor. Power Query allows for more robust error handling and can enable query folding, optimizing performance.

How do I create a custom column that conditionally converts a number?

Use an if statement with Value.Is() and try...otherwise. For example: each if Value.Is([MyColumn], type text) then try Number.From([MyColumn]) otherwise null else [MyColumn]. This attempts conversion only if it’s text, otherwise keeps the original value.

What is query folding and how does it relate to number types?

Query folding is when Power Query translates your transformation steps into the native query language of your data source (e.g., SQL). Early and correct type conversions (like from text to number) are essential for query folding to occur, allowing the data source to process the transformations, which significantly speeds up data refreshes for large datasets.

Why do I see `null` values after converting a column to a number type?

You might see null values if the original column contained entries that could not be converted into a number (e.g., blank cells, specific text values that were replaced with null during error handling, or values that failed a try...otherwise conversion).

What is the maximum value for `Int64.Type`?

The maximum positive value for Int64.Type is 9,223,372,036,854,775,807. This makes it suitable for almost all whole number scenarios encountered in business data. Textron tsv login

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Power query type number

Understanding Numerical Data Types in Power Query

Diving Deep into `type number` (Decimal Number)

`Int64.Type` vs. `type number`: Choosing the Right Whole Number Type

Managing Mixed Data Types: `power query data type number and text`

`power query type nullable number`: Handling Missing Numerical Values

`power query change type to number`: Step-by-Step Conversion and Error Handling

`power query if type is number`: Conditional Logic and Type Checking

`power bi type decimal number`: Formatting vs. Data Type in Power BI

Performance and Best Practices for Numerical Types

FAQ

What is `type number` in Power Query?

How do I change a column to `type number` in Power Query?

What is the difference between `type number` and `Int64.Type`?

When should I use `Int64.Type` instead of `type number`?

How do I convert a column to “2 decimal places” in Power Query?

What does `type nullable number` mean in Power Query?

How do I handle `power query data type number and text` issues?

What is `power bi type decimal number`?

Can Power Query automatically convert text to number?

How do I check if a value is a number in Power Query (e.g., `power query if type is number`)?

What happens if I convert a decimal number to `Int64.Type`?

How do I replace errors after a type conversion in Power Query?

Why is my number column showing “ABC 123” icon in Power Query?

Is `type currency` better than `type number` for financial data?

How does changing data types affect Power BI performance?

Can I change `power bi data type number and text` directly in Power BI Desktop?

How do I create a custom column that conditionally converts a number?

What is query folding and how does it relate to number types?

Why do I see `null` values after converting a column to a number type?

What is the maximum value for `Int64.Type`?

Comments

Leave a Reply Cancel reply

Power query type number

Understanding Numerical Data Types in Power Query

Diving Deep into type number (Decimal Number)

Int64.Type vs. type number: Choosing the Right Whole Number Type

Managing Mixed Data Types: power query data type number and text

power query type nullable number: Handling Missing Numerical Values

power query change type to number: Step-by-Step Conversion and Error Handling

power query if type is number: Conditional Logic and Type Checking

power bi type decimal number: Formatting vs. Data Type in Power BI

Performance and Best Practices for Numerical Types

FAQ

What is type number in Power Query?

How do I change a column to type number in Power Query?

What is the difference between type number and Int64.Type?

When should I use Int64.Type instead of type number?

How do I convert a column to “2 decimal places” in Power Query?

What does type nullable number mean in Power Query?

How do I handle power query data type number and text issues?

What is power bi type decimal number?

Can Power Query automatically convert text to number?

How do I check if a value is a number in Power Query (e.g., power query if type is number)?

What happens if I convert a decimal number to Int64.Type?

How do I replace errors after a type conversion in Power Query?

Why is my number column showing “ABC 123” icon in Power Query?

Is type currency better than type number for financial data?

How does changing data types affect Power BI performance?

Can I change power bi data type number and text directly in Power BI Desktop?

How do I create a custom column that conditionally converts a number?

What is query folding and how does it relate to number types?

Why do I see null values after converting a column to a number type?

What is the maximum value for Int64.Type?

Comments

Leave a Reply Cancel reply

Diving Deep into `type number` (Decimal Number)

`Int64.Type` vs. `type number`: Choosing the Right Whole Number Type

Managing Mixed Data Types: `power query data type number and text`

`power query type nullable number`: Handling Missing Numerical Values

`power query change type to number`: Step-by-Step Conversion and Error Handling

`power query if type is number`: Conditional Logic and Type Checking

`power bi type decimal number`: Formatting vs. Data Type in Power BI

What is `type number` in Power Query?

How do I change a column to `type number` in Power Query?

What is the difference between `type number` and `Int64.Type`?

When should I use `Int64.Type` instead of `type number`?

What does `type nullable number` mean in Power Query?

How do I handle `power query data type number and text` issues?

What is `power bi type decimal number`?

How do I check if a value is a number in Power Query (e.g., `power query if type is number`)?

What happens if I convert a decimal number to `Int64.Type`?

Is `type currency` better than `type number` for financial data?

Can I change `power bi data type number and text` directly in Power BI Desktop?

Why do I see `null` values after converting a column to a number type?

What is the maximum value for `Int64.Type`?